Predicting anticancer synergistic drug combinations based on multi-task learning

Background The discovery of anticancer drug combinations is a crucial work of anticancer treatment. In recent years, pre-screening drug combinations with synergistic effects in a large-scale search space adopting computational methods, especially deep learning methods, is increasingly popular with researchers. Although achievements have been made to predict anticancer synergistic drug combinations based on deep learning, the application of multi-task learning in this field is relatively rare. The successful practice of multi-task learning in various fields shows that it can effectively learn multiple tasks jointly and improve the performance of all the tasks. Methods In this paper, we propose MTLSynergy which is based on multi-task learning and deep neural networks to predict synergistic anticancer drug combinations. It simultaneously learns two crucial prediction tasks in anticancer treatment, which are synergy prediction of drug combinations and sensitivity prediction of monotherapy. And MTLSynergy integrates the classification and regression of prediction tasks into the same model. Moreover, autoencoders are employed to reduce the dimensions of input features. Results Compared with the previous methods listed in this paper, MTLSynergy achieves the lowest mean square error of 216.47 and the highest Pearson correlation coefficient of 0.76 on the drug synergy prediction task. On the corresponding classification task, the area under the receiver operator characteristics curve and the area under the precision–recall curve are 0.90 and 0.62, respectively, which are equivalent to the comparison methods. Through the ablation study, we verify that multi-task learning and autoencoder both have a positive effect on prediction performance. In addition, the prediction results of MTLSynergy in many cases are also consistent with previous studies. Conclusion Our study suggests that multi-task learning is significantly beneficial for both drug synergy prediction and monotherapy sensitivity prediction when combining these two tasks into one model. The ability of MTLSynergy to discover new anticancer synergistic drug combinations noteworthily outperforms other state-of-the-art methods. MTLSynergy promises to be a powerful tool to pre-screen anticancer synergistic drug combinations.


1
This report gives supplementary information to the manuscript "Predicting Anticancer Synergistic Drug Combinations Based on Multi-task Learning".It provides more detailed experimental data.
We adopt 5-fold nested cross-validation in the experiments, the process is as follows: In the 5-fold nested cross-validation, we employ 4-folds (3-folds for inner training and the other for inner validating) to select the optimal hyperparameters in the inner loop based on the validation loss.In the outer loop, we train the model with searched optimal hyperparameters on these 4-folds, and the 4-folds are randomly split into an outer training set and an outer validation set in a ratio of 9:1.The remaining fold is utilized as a test set to evaluate the trained model.In this process, every fold is selected as the test set in turn, so the results we report include the mean and standard deviation on the 5-fold data.The hyperparameters of the two machine learning methods to be searched and their candidate values are listed in Table S1.S13 and S14. Figure S1 summarizes the PCC between the predicted scores and the ground truth on each drug, and the colors of bars shows the targets of drugs.The PCC of MTLSynergy across drugs ranges from 0.58 (DINACICLIB) to 0.83 (ETOPOSIDE).Among the 38 drugs, only 4 drugs present a PCC lower than 0.65, whereas 18 drugs (47.37%) exhibit a PCC higher than 0.75.There is no clear association between targets and correlation can be observed.0.70.Overall, synergy scores predicted by MTLSynergy show a strong correlation with the ground truth in different drugs, and no clear association between PCC and targets is observed.

Figure S1 :
Figure S1: PCC values of each drug.The color of the bar represents the target of the drug.

Table S1 :
Hyperparameters and candidate values for machine learning models TablesS2-S11show the results of different methods on different folds.

Table S2 :
Synergy prediction results of MTLSynergy on each fold.

Table S4 :
Synergy prediction results of Random Forest on each fold.

Table S5 :
Synergy prediction results of OnlySynergy on each fold.

Table S6 :
Synergy prediction results of MTLSynergy-NoAE on each fold.

Table S7 :
Synergy prediction results of MTLSynergy-Regression on each fold.

Table S8 :
Sensitivity prediction results of MTLSynergy on each fold.

Table S9 :
Sensitivity prediction results of OnlySensitivity on each fold.

Table S10 :
Sensitivity prediction results of MTLSynergy-NoAE on each fold.

Table S11 :
Sensitivity prediction results of MTLSynergy-Regression on each fold.We also list the detailed results of MTLSynergy on different dimensions.The output dimension of the drug encoder is selected from {32, 64, 128, 256, 512}, and the output dimension of the cell line encoder is chosen from {128, 256, 512, 1024, 2048}.We only modify one of or in each experiment and keep the other unchanged.

Table S12 :
Results of MTLSynergy on different dimensions.We evaluate MTLSynergy, DeepSynergy, Gradient Tree Boosting, and Random Forest using the same feature data on the Leave Drugs Out scenario (samples are split to make that drugs seen in the test set are not in the training set) and on the Leave Cell Lines Out scenario (samples are split to make that cell lines seen in the test set are not in the training set), respectively.The detailed results are shown in Tables

Table S13 :
Results of the method comparison in the Leave Drug Out scenario

Table S14 :
Results of the method comparison in the Leave Cell Line Out scenario